High resolution and accurate rainfall information is essential to modeling and predicting hydrological processes. Crowdsourced personal weather stations (PWSs) have become increasingly popular in recent years and can provide dense spatial and temporal resolution in rainfall estimates. However, their usefulness is limited due to a lack of trust in crowdsourced data compared to traditional data sources. Using crowdsourced PWSs data without an evaluation of its trustworthiness can result in inaccurate rainfall estimates as PWSs may be poorly maintained or incorrectly sited. In this study, we advance the Reputation System for Crowdsourced Rainfall Networks (RSCRN) to bridge this trust gap by assigning dynamic trust scores to the PWSs. Using rainfall data collected from 18 PWSs in two dense clusters in Houston, Texas USA as a case study, the results show that using RSCRN-derived trust scores can increase the accuracy of 15-min PWS rainfall estimates when compared to rainfall observations recorded at city’s high-fidelity rainfall stations. Overall, RSCRN rainfall estimates improved for 77% (48 out of 62) of the analyzed storm events, with a median RMSE improvement of 27.3%. Compared to an existing PWS quality control method, results showed that while 13 (21%) storm events had the same performance, RSCRN improved rainfall estimates for 78% of the remaining storm events (38 out of 49), with a median RMSE improvement of 13.4%. Using RSCRN-derived trust scores can make the rapidly growing network of PWSs a more useful resource for urban flood management, greatly improving knowledge of rainfall patterns in areas with dense PWSs.